Post-Training ML Engineer: Fine-Tune & Evaluate LMs

Reinforced Learning; Town of Tonawanda (NY); On-site; USD 120,000 - 160,000; Full time

Job summary

Reinforced Learning seeks ML engineers to build end-to-end RL/LM training pipelines in a research engineering setting. You’ll implement SFT and RL post-training workflows to isolate dataset effects and run comparable evaluations.

You’ll design experiment tracking, checkpoint management, and evaluation plumbing, and integrate with customer training stacks where programs require it, delivering concise reports with confounders named.

Qualifications

• Two or more years in ML engineering or research engineering.
• Hands-on fine-tuning of language models and running evaluations.
• Strong Python and systems debugging across data, compute and model behaviour.
• You ship reproducible pipelines.

Responsibilities

• SFT and RL post-training pipelines on open models.
• Controlled experiments that measure what a dataset changes.
• Experiment tracking, checkpoint management and evaluation plumbing.
• Integration with customer training stacks where programs require it.
• Concise reports with the confounders named.

Skills

ML engineering

Python

System debugging

Reproducible pipelines

Fine-tuning language models

Tools

Distributed training frameworks

Cluster schedulers

Job description

Reinforced Learning seeks ML engineers to build end-to-end RL/LM training pipelines in a research engineering setting. You’ll implement SFT and RL post-training workflows to isolate dataset effects and run comparable evaluations.

You’ll design experiment tracking, checkpoint management, and evaluation plumbing, and integrate with customer training stacks where programs require it, delivering concise reports with confounders named.